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Radiology Journal Submission Guidelines

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing the stringent methodological and reporting standards required for submitting medical imaging research (diagnostic, AI, interventional) to a top-tier radiology journal. It covers expectations for diagnostic accuracy (STARD), AI model validation (CLAIM), sample size justification, reproducibility, and ensuring clinical relevance, helping authors frame their work for optimal journal fit.

Assessing Fit for Top IO Journals

rand-journal-of-economics

brycewang-stanford/Awesome-Journal-Skills

This tool guides authors writing on industrial organization, market structure, regulation, and antitrust. It provides a rigorous framework for evaluating whether a manuscript meets the high standards of top-tier IO journals by assessing theoretical models, empirical identification, and policy significance. Use it for venue selection and manuscript re-framing.

Designing Rigorous Recommender System Experiments

recsys-experiments

brycewang-stanford/Awesome-Journal-Skills

A comprehensive framework for designing, auditing, and reporting empirical results in recommender systems research. It covers best practices for handling temporal splits, mitigating evaluation bias (e.g., exposure/popularity), utilizing advanced off-policy estimators (IPS, SNIPS), and structuring the critical link between offline metrics and real-world A/B test results. Essential for academic submissions.

Ensuring Recommender System Reproducibility

recsys-reproducibility

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

Navigating Conference Paper Review Strategy

recsys-review-process

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide to the rigorous academic peer review process, exemplified by top-tier Recommender Systems conferences (RecSys). It details the double-blind review mechanics, key evaluation criteria (novelty, validity, reproducibility), the critical rebuttal phase, and the requirements for final publication in ACM Digital Library. Essential for researchers and students aiming to improve academic submission quality.

Structuring Supplementary Material For Research Papers

recsys-supplementary

brycewang-stanford/Awesome-Journal-Skills

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

Guiding Your Project To RecSys Fit

recsys-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This guide helps researchers determine if their project's core contribution is genuinely centered on recommender systems. It provides a structured fit test to route the work to the most appropriate top-tier venue (RecSys, SIGIR, KDD, etc.) and select the correct conference track before starting the writing process.

Recommender Systems Project Workflow Planner

recsys-workflow

brycewang-stanford/Awesome-Journal-Skills

A comprehensive project management skill designed for academic submissions in Recommender Systems (RecSys). It guides users through the entire research lifecycle, from initial venue fit and data freezing to abstract submission, full paper writing, anonymous repository management, rebuttal drafting, and final camera-ready preparation. It provides backward-planning time offsets and risk identification.

Quantitative Analysis for Economic Dynamics

red-data-analysis

brycewang-stanford/Awesome-Journal-Skills

This guide outlines the rigorous standards for quantitative analysis required in academic economic research, particularly dynamic modeling. It provides a framework covering systematic model calibration, moment estimation, numerical solution discipline, and establishing fully reproducible computation workflows, ensuring results are highly auditable and verifiable.

Identification and Model Logic for Economic Dynamics

red-identification-strategy

brycewang-stanford/Awesome-Journal-Skills

This comprehensive guide helps authors structure the inferential backbone of economic papers for top journals. It provides detailed checklists for theoretical models (assumptions, regularity), empirical studies (causal design, identification), and computational methods (convergence, asymptotics), ensuring claims are robust and credible across different paper types.

Replication and Data Policy for Economics Research

red-replication-and-data-policy

brycewang-stanford/Awesome-Journal-Skills

This guide establishes the rigorous policy for submitting computational and empirical papers to academic journals. It mandates that authors provide sufficient data, source code, and detailed documentation (including execution order, random seeds, and OS used) to ensure that research findings can be fully reproduced by others, adhering to global standards like Option C.

Designing Quantitative Exhibits for Macroeconomics

red-tables-figures

brycewang-stanford/Awesome-Journal-Skills

This guide outlines best practices for creating quantitative exhibits in dynamic macroeconomics papers. It covers crucial components like Impulse-Response Functions (IRFs), calibration tables, moment-matching results, and policy counterfactuals. The focus is on ensuring the model's mechanism and quantitative fit are clear, legible, and self-contained for expert review.

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